Abstract
This study explores the capacity of the improved empirical mode decomposition (EMD) in railway wheel flat detection. Aiming at the mode mixing problem of EMD, an EMD energy conservation theory and an intrinsic mode function (IMF) superposition theory are presented and derived, respectively. Based on the above two theories, an improved EMD method is further proposed. The advantage of the improved EMD is evaluated by a simulated vibration signal. Then this method is applied to study the axle box vibration response caused by wheel flats, considering the influence of both track irregularity and vehicle running speed on diagnosis results. Finally, the effectiveness of the proposed method is verified by a test rig experiment. Research results demonstrate that the improved EMD can inhibit mode mixing phenomenon and extract the wheel fault characteristic effectively.
Highlights
Wheel flat is the most common local surface defect in railway wheels
We present a detection method for wheel flats based on the measurement of axle box vibration by installed accelerometers
There is a favorable match between the estimated features and the actual fault features associated with the railway wheel with flat
Summary
Wheel flat is the most common local surface defect in railway wheels. It can result in cyclic wheel-rail impact during the running process and cause coupled vibration in the entire vehicle-track system [1]. Wheels with this fault affect the running safety significantly and cause further damage to the wheels and rails. It is of great interest among researchers to find out effective methods for early detection and identification of wheel flats. Apart from the above methods, some optical, mechanical, and supersonic systems are attempted to apply in the identification of wheel flat
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